- Interesting Questions
- Why is it important
- Datasets
- Datasets used (source)
- Variables and Proxy Variables
- Potential Weaknesses
- Data Visualization
- Technics used for visualizing data
- Result Plots
- Conclusions
- Q & A
2/28/2021
## # A tibble: 6 x 8 ## country year EnergyUse_pp CO2_Emissions_pp gdpPercap pop Coal_consumption ## <fct> <int> <dbl> <dbl> <dbl> <int> <dbl> ## 1 Algeria 1971 233 1.26 7170 1.49e7 23000 ## 2 Algeria 1972 263 1.85 8410 1.53e7 20000 ## 3 Algeria 1973 307 2.44 8420 1.57e7 23000 ## 4 Algeria 1974 321 1.98 8670 1.61e7 19000 ## 5 Algeria 1975 332 1.93 8990 1.66e7 24000 ## 6 Algeria 1976 369 2.29 9290 1.71e7 47000 ## # ... with 1 more variable: continent <fct>
Left_join (combine data)pivot_longer (pivot data from wide to long)mutate (add column for data)ggplot (make plots)plotly (create data visualization)Countrycode package (assign continent for each country)Shiny appshinyAppFile("Energy.R", options = list())
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summary(cars)
## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00
plot(pressure)